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Hybrid Approach Combining Model-Based Method with the Technology of Machine Learning for Forecasting of Dangerous Weather Phenomena

  • Elena N. StankovaEmail author
  • Irina A. Grechko
  • Yana N. Kachalkina
  • Evgeny V. Khvatkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10408)

Abstract

The paper is a continuation of the works [1, 2, 3, 4] where has been shown how the technologies of machine learning and online analytical processing (OLAP) could be used in conjunction with the numerical model of convective cloud for forecasting dangerous convective phenomena such as thunderstorm, heavy rainfall and hail. We study specifically the possibility of making predictions via a hybrid approach that combines the predictive numerical model of convective cloud with the modern methods of big data processing. We overview the existing examples of using of machine learning tools for weather forecasting and discuss the range of their applicability.

Keywords

OLAP Online analytical processing Machine learning Validation of numerical models Numerical model of convective cloud Weather forecasting Thunderstorm Multidimensional data base Data mining 

Notes

Acknowledgment

This research was sponsored by the Russian Foundation for Basic Research under the projects: No. 16-07-01113.

References

  1. 1.
    Petrov, D.A., Stankova, E.N.: Use of consolidation technology for meteorological data processing. In: Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2014. LNCS, vol. 8579, pp. 440–451. Springer, Cham (2014). doi: 10.1007/978-3-319-09144-0_30 Google Scholar
  2. 2.
    Petrov, D.A., Stankova, E.N.: Integrated information system for verification of the models of convective clouds. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9158, pp. 321–330. Springer, Cham (2015). doi: 10.1007/978-3-319-21410-8_25 CrossRefGoogle Scholar
  3. 3.
    Stankova, E.N., Petrov, D.A.: Complex information system for organization of the input data of models of convective clouds. Appl. Math. Comput. Sci. Control Process. 10(3), 83–95 (2015). Vestnik of Saint-Petersburg University. (in Russian)Google Scholar
  4. 4.
    Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Shorov, A.V., Korkhov, V.V.: Using technologies of OLAP and machine learning for validation of the numerical models of convective clouds. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O., Stankova, E., Wang, S. (eds.) ICCSA 2016. LNCS, vol. 9788, pp. 463–472. Springer, Cham (2016). doi: 10.1007/978-3-319-42111-7_36 CrossRefGoogle Scholar
  5. 5.
    Grover, A., Kapoor, A., Horvitz, E.: A Deep Hybrid Model for Weather Forecasting (2015). http://research.microsoft.com/en-us/um/people/horvitz/weather_hybrid_representation.pdf. Accessed 13 Aug 2016
  6. 6.
    Meteum technology. https://yandex.ru/pogoda/meteum. Accessed 17 Jan 2017. (in Russian)
  7. 7.
    The Weather Company Launches ‘Deep Thunder’ - the World’s Most Advanced Hyper-Local Weather Forecasting Model for Businesses https://www-03.ibm.com/press/us/en/pressrelease/49954.wss. Accessed 15 June 2016
  8. 8.
    Using Amazon Machine Learning to Predict the Weather. https://arnesund.com/2015/05/31/using-amazon-machine-learning-to-predict-the-weather/. Accessed 20 Feb 2017
  9. 9.
    Tutorial: Using Amazon ML to Predict Responses to a Marketing Offer. http://docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html. Accessed 10 Mar 2017
  10. 10.
    How Yandex predicts the weather. https://yandex.ru/company/technologies/meteum/. Accessed 17 Jan 2017. (in Russian)
  11. 11.
    Weather Research and Forecasting Model. http://www.wrf-model.org/index.php. Accessed 7 Mar 2017
  12. 12.
    Matrixnet. https://yandex.ru/company/technologies/matrixnet/. Accessed 26 Nov 2016. (in Russian)
  13. 13.
    Grover, A., Kapoor, A., Horvitz, E.: A Deep Hybrid Model for Weather Forecasting (2015). http://research.microsoft.com/en-us/um/people/horvitz/weather_hybrid_representation.pdf. Accessed 13 Aug 2016
  14. 14.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of Statistical Learning (2009). http://statweb.stanford.edu/~tibs/ElemStatLearn/. Accessed 15 Feb 2015
  15. 15.
    Mitchell, T.: Machine Learning. Springer, Berlin (2009)zbMATHGoogle Scholar
  16. 16.
    Codd, E.: Providing OLAP (on-line analytical processing) to user-analysts: an IT mandate, Technical report, E.F. Codd and Associates (1993)Google Scholar
  17. 17.
    Raba, N., Stankova, E.: Research of influence of compensating descending flow on cloud’s life cycle by means of 1.5-dimensional model with 2 cylinders. Proc. MGO 559, 192–209 (2009). (in Russian)Google Scholar
  18. 18.
    Raba, N., Stankova, E., Ampilova, N.: On investigation of parallelization effectiveness with the help of multi-core processors. Procedia Comput. Sci. 1(1), 2757–2762 (2010)CrossRefGoogle Scholar
  19. 19.
    Raba, N., Stankova, E.: On the possibilities of multi-core processor use for real-time forecast of dangerous convective phenomena. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, Bernady O. (eds.) ICCSA 2010. LNCS, vol. 6017, pp. 130–138. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-12165-4_11 CrossRefGoogle Scholar
  20. 20.
    Raba, N.O., Stankova, E.N.: On the Problem of Numerical Modeling of Dangerous Convective Phenomena: Possibilities of Real-Time Forecast with the Help of Multi-core Processors. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, Bernady O. (eds.) ICCSA 2011. LNCS, vol. 6786, pp. 633–642. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21934-4_51 CrossRefGoogle Scholar
  21. 21.
    Raba, N.O., Stankova, E.N.: On the effectiveness of using the GPU for numerical solution of stochastic collection equation. In: Murgante, B., Misra, S., Carlini, M., Torre, Carmelo M., Nguyen, H.-Q., Taniar, D., Apduhan, Bernady O., Gervasi, O. (eds.) ICCSA 2013. LNCS, vol. 7975, pp. 248–258. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-39640-3_18 CrossRefGoogle Scholar
  22. 22.
    Scikit-learn: Machine Learning in Python. http://scikit-learn.org/
  23. 23.
    Jedox. http://www.jedox.com/. Accessed 21 Nov 2015
  24. 24.
    Gankevich, I., Korkhov, V., Balyan, S., Gaiduchok, V., Gushchanskiy, D., Tipikin, Y., Degtyarev, A., Bogdanov, A.: Constructing virtual private supercomputer using virtualization and cloud technologies. In: Murgante, B., Misra, S., Rocha, Ana Maria A.C., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, Bernady O., Gervasi, O. (eds.) ICCSA 2014. LNCS, vol. 8584, pp. 341–354. Springer, Cham (2014). doi: 10.1007/978-3-319-09153-2_26 Google Scholar
  25. 25.
    Gankevich, I., Gaiduchok, V., Gushchanskiy, D., Tipikin, Y., Korkhov, V., Degtyarev, A., Bogdanov, A., Zolotarev, V.: Virtual private supercomputer: design and evaluation. In: Computer Science and Information Technologies (CSIT), pp 1–6. IEEE Conference Publications (2013). doi: 10.1109/CSITechnol.2013.6710358

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Elena N. Stankova
    • 1
    • 2
    Email author
  • Irina A. Grechko
    • 2
  • Yana N. Kachalkina
    • 2
  • Evgeny V. Khvatkov
    • 1
  1. 1.Saint-Petersburg State UniversitySt. PetersburgRussia
  2. 2.Saint-Petersburg Electrotechnical University “LETI”, (SPbETU)St. PetersburgRussia

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